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Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin

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posted on 2022-06-21, 02:02 authored by Jean BertheletJean Berthelet, Momeneh Foroutan, Dharmesh D Bhuva, Holly J Whitfield, Farrah El-SaafinFarrah El-Saafin, Joseph Cursons, Antonin SerranoAntonin Serrano, Michal Merdas, Elgene Lim, Emmanuelle Charafe-Jauffret, Christophe Ginestier, Matthias ErnstMatthias Ernst, Frederic Hollande, Robin AndersonRobin Anderson, Bhupinder PalBhupinder Pal, Belinda YeoBelinda Yeo, Melissa J Davis, Delphine MerinoDelphine Merino
The development of therapies that target specific disease subtypes has dramatically improved outcomes for patients with breast cancer. However, survival gains have not been uniform across patients, even within a given molecular subtype. Large collections of publicly available drug screening data matched with transcriptomic measurements have facilitated the development of computational models that predict response to therapy. Here, we generated a series of predictive gene signatures to estimate the sensitivity of breast cancer samples to 90 drugs, comprising FDA-approved drugs or compounds in early development. To achieve this, we used a cell line-based drug screen with matched transcriptomic data to derive in silico models that we validated in large independent datasets obtained from cell lines and patient-derived xenograft (PDX) models. Robust computational signatures were obtained for 28 drugs and used to predict drug efficacy in a set of PDX models. We found that our signature for cisplatin can be used to identify tumors that are likely to respond to this drug, even in absence of the BRCA-1 mutation routinely used to select patients for platinum-based therapies. This clinically relevant observation was confirmed in multiple PDXs. Our study foreshadows an effective delivery approach for precision medicine.

Funding

The Olivia Newton-John Cancer Research Institute acknowledges the support of the Operational Infrastructure Program of the Victorian Government. D.M., M.J.D. and B.Y. are supported by the Grant-in-Aid Scheme administered by Cancer Council Victoria. D.M., B.Y. and R.L.A. are supported by Love Your Sister. M.J.D. is supported by the Betty Smyth Centenary Fellowship, National Breast Cancer Foundation (NBCF-ECF-043-14), the Cure Brain Cancer Foundation and National Breast Cancer Foundation joint grant (CBCNBCF-19-009) as well as NHMRC Project Grants APP1128609 and AP1141361. D.M., F.H., and B.P. are supported by the NBCF (Investigator Initiated Research Grant IIRS-19-082). D.M. is supported by Susan G. Komen and Cancer Australia (CCR19606878). F.H. is supported by the National Health and Medical Research Council of Australia (Grant #1164081) and by the Tour de Cure Foundation. R.L.A. acknowledges fellowship support from NBCF (CF-09-01). The authors and Olivia Newton-John Cancer Research Institute gratefully acknowledge the generous support of the Love Your Sister Foundation. The contents of the published material are solely the responsibility of the individual authors and do not reflect the views of Cancer Australia and other funding agencies.

History

Publication Date

2022-05-01

Journal

Cancers

Volume

14

Issue

10

Article Number

2404

Pagination

18p.

Publisher

Multidisciplinary Digital Publishing Institute (MDPI)

ISSN

2072-6694

Rights Statement

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.